Let’s start with a question… You are hosting a children’s party and estimate you need 100 party packs for the event. If one party pack costs $5, how much does it cost for all the packs?
This is the type of question famous quant and founder of the Certificate in Quantitative Finance (CQF) Dr. Paul Wilmott is likely to ask you while teaching quantitative modelling. Quantitative finance in its most basic form is about answering such questions, a combination of mathematics, finance and computing.
The answer to this question may seem obvious (we will come back to a solution later) but demonstrates some of the thought processes needed to be a quant.
Who are the quants?
Once known as the “rocket scientists” of Wall Street, more recently called the “math wizards” who nearly destroyed the street, quantitative finance has become an integral part of modern finance.
The subject draws on a rich history of ideas dating back to the early 19th century where Scottish botanist, Robert Brown gave his name to the random motion of particles, Albert Einstein who later proposed a scientific foundation for “Brownian” motion, to the more familiar names in finance such as Markowitz, Sharpe, Black, Scholes and Merton who used and built on these and other ideas to develop the financial models we use today.
The quant revolution started 60 years ago in academic finance, but more recently made its way to Wall Street when a group of scientists started applying techniques such as stochastic calculus and probability theory, once reserved for hard-core, scientific research, into the area of option pricing and financial modelling. Over the last 20 years its rise to prominence and usage has been closely linked with advancements in technology.
The power of computers has meant that we can now run millions of simulations, solve complex mathematical equations and analyse enormous data sets in the blink of an eye, leading to rapid innovation of products and markets, benefiting not just the industry but the global economy as a whole.
Where do quants work?
It could be argued that all areas of finance require quantitative skills, but the term “quants” has always been reserved for a small group within the industry who use mathematics and computer science to model problems within finance.
Originally quants were mainly located on the sell side working on derivatives pricing, but over the years both the definition and location has expanded to cover most areas where mathematics is applied in any meaningful way to solve a financial problem in both the sell side and buy side.
When we started training quants back in 2003, most delegates tended to come from areas such as derivatives, risk management, structuring and trading. Over the years we have seen a significant diversification of the types of roles who need this skills set. There has been an increase of delegates from the buy side such as hedge funds and asset management, IT departments, the insurance sector, energy trading and post global financial crisis new control functions such as model validation and independent price verification.
As quantitative skills have become more important in the industry, the need for more people to understand the models and very importantly the assumptions behind the models has increased.
What makes a good quant?
There are three main ingredients to quantitative finance, they are finance, mathematics and computer science.
It is through the combination of these disciplines that quantitative finance was born and although the industry changed dramatically over the last few decades these combinations are still in great demand.
banks no longer just look for technical talent but are also keen to hire people who have a healthy interest in the financial markets
In the 1990s and the early 2000s it was sufficient to hire a smart mathematician or physicist with little or no finance or markets knowledge, however the financial crisis has shown us what overreliance on mathematical models can lead to. Post financial crisis, banks no longer just look for technical talent but are also keen to hire people who have a healthy interest in the financial markets. Hiring individuals who are able to critique the models, understanding where they work, but more importantly where they do not work has become essential.
The philosophy can be summed up by the Modellers’ Hippocratic Oath1, established by Dr. Paul Wilmott and Dr. Emanuel Derman following the financial crisis:
The Modellers’ Hippocratic Oath
I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
- Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
- I will never sacrifice reality for elegance without explaining why I have done so.
- Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
- I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.
Back to the question
Now going back to the original question, your immediate response may have been $500, it’s a simple calculation $5 x 100 = $500, right?
Wrong! This is the world of finance and a simple linear model, which may be appropriate when modelling a physics or engineering problem does not apply. In the real world we would ask for a discount, or shop around and get some type of deal, or on the other hand we may come across some unscrupulous retailer who knows you have a party and also knows that your car is in a local garage and so you have no way of going to another store some 30 miles away. He will want $8 for each pack! One thing we know for sure is that applying a simple linear model to this problem will not best reflect reality and will risk our money in the long run.
The future of quant finance
The question is a good example of why quantitative finance is not just about the mathematics. Financial models are exactly that, models for finance not scientific theories, they are full of assumptions and far from perfect. This is why the best skill a quant can learn is to be sceptical, yes of course you have to learn the mathematics, the computing skills, and the financial theory, but never forget that quantitative finance is not a branch of pure mathematics and therefore understanding the context of the problem and a big dose of common sense is always needed.
Quantitative finance will be integral to the future growth of the financial services industry. As finance becomes more technical the need for more people, from a variety of roles, to understand this domain has never been greater. Quantitative finance is here to stay and knowledge of this area will become the modern benchmark of financial education.